




import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class 						UpperCamelCase__      (__lowerCAmelCase       ,							unittest.TestCase	):

    '''simple docstring'''



    lowerCamelCase_ :      Optional[Any]      	= UnCLIPImageVariationPipeline
    lowerCamelCase_ :      Optional[int]      	= IMAGE_VARIATION_PARAMS - {"""height""", """width""", """guidance_scale"""}
    lowerCamelCase_ :      Tuple      	= IMAGE_VARIATION_BATCH_PARAMS

    lowerCamelCase_ :      Dict      	= [
        """generator""",
        """return_dict""",
        """decoder_num_inference_steps""",
        """super_res_num_inference_steps""",
    ]
    lowerCamelCase_ :      Any      	= False







    @property
    def        _lowercase	(       self	)	-> List[Any]:
           return 32







    @property
    def        _lowercase	(       self	)	-> Optional[int]:
           return 32







    @property
    def        _lowercase	(       self	)	-> List[str]:
           return self.time_input_dim







    @property
    def        _lowercase	(       self	)	-> Any:
           return self.time_input_dim * 4







    @property
    def        _lowercase	(       self	)	-> Optional[Any]:
           return 100







    @property
    def        _lowercase	(       self	)	-> str:
           lowerCamelCase  : str     					=   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)
           return tokenizer







    @property
    def        _lowercase	(       self	)	-> Dict:
           torch.manual_seed(0	)
           lowerCamelCase  : Any     					=   CLIPTextConfig(
               bos_token_id=0	,    eos_token_id=2	,    hidden_size=self.text_embedder_hidden_size	,    projection_dim=self.text_embedder_hidden_size	,    intermediate_size=37	,    layer_norm_eps=1e-05	,    num_attention_heads=4	,    num_hidden_layers=5	,    pad_token_id=1	,    vocab_size=1000	,    )
           return CLIPTextModelWithProjection(lowerCamelCase_	)







    @property
    def        _lowercase	(       self	)	-> Any:
           torch.manual_seed(0	)
           lowerCamelCase  : str     					=   CLIPVisionConfig(
               hidden_size=self.text_embedder_hidden_size	,    projection_dim=self.text_embedder_hidden_size	,    num_hidden_layers=5	,    num_attention_heads=4	,    image_size=32	,    intermediate_size=37	,    patch_size=1	,    )
           return CLIPVisionModelWithProjection(lowerCamelCase_	)







    @property
    def        _lowercase	(       self	)	-> str:
           torch.manual_seed(0	)

           lowerCamelCase  : int     					=   {
               "clip_embeddings_dim": self.text_embedder_hidden_size,
               "time_embed_dim": self.time_embed_dim,
               "cross_attention_dim": self.cross_attention_dim,
           }

           lowerCamelCase  : Optional[Any]     					=   UnCLIPTextProjModel(**lowerCamelCase_	)
           return model







    @property
    def        _lowercase	(       self	)	-> Union[str, Any]:
           torch.manual_seed(0	)

           lowerCamelCase  : Optional[Any]     					=   {
               "sample_size": 32,
               # RGB in channels
               "in_channels": 3,
               # Out channels is double in channels because predicts mean and variance
               "out_channels": 6,
               "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
               "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
               "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
               "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
               "layers_per_block": 1,
               "cross_attention_dim": self.cross_attention_dim,
               "attention_head_dim": 4,
               "resnet_time_scale_shift": "scale_shift",
               "class_embed_type": "identity",
           }

           lowerCamelCase  : Dict     					=   UNetaDConditionModel(**lowerCamelCase_	)
           return model







    @property
    def        _lowercase	(       self	)	-> Optional[Any]:
           return {
               "sample_size": 64,
               "layers_per_block": 1,
               "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
               "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
               "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
               "in_channels": 6,
               "out_channels": 3,
           }







    @property
    def        _lowercase	(       self	)	-> int:
           torch.manual_seed(0	)

           lowerCamelCase  : List[Any]     					=   UNetaDModel(**self.dummy_super_res_kwargs	)
           return model







    @property
    def        _lowercase	(       self	)	-> int:
           torch.manual_seed(1	)

           lowerCamelCase  : List[Any]     					=   UNetaDModel(**self.dummy_super_res_kwargs	)
           return model







    def        _lowercase	(       self	)	-> Dict:
           lowerCamelCase  : Dict     					=   self.dummy_decoder
           lowerCamelCase  : str     					=   self.dummy_text_proj
           lowerCamelCase  : Union[str, Any]     					=   self.dummy_text_encoder
           lowerCamelCase  : Any     					=   self.dummy_tokenizer
           lowerCamelCase  : Optional[int]     					=   self.dummy_super_res_first
           lowerCamelCase  : Tuple     					=   self.dummy_super_res_last

           lowerCamelCase  : str     					=   UnCLIPScheduler(
               variance_type="learned_range"	,    prediction_type="epsilon"	,    num_train_timesteps=1000	,    )

           lowerCamelCase  : List[Any]     					=   UnCLIPScheduler(
               variance_type="fixed_small_log"	,    prediction_type="epsilon"	,    num_train_timesteps=1000	,    )

           lowerCamelCase  : Optional[Any]     					=   CLIPImageProcessor(crop_size=32	,    size=32	)

           lowerCamelCase  : Any     					=   self.dummy_image_encoder

           return {
               "decoder": decoder,
               "text_encoder": text_encoder,
               "tokenizer": tokenizer,
               "text_proj": text_proj,
               "feature_extractor": feature_extractor,
               "image_encoder": image_encoder,
               "super_res_first": super_res_first,
               "super_res_last": super_res_last,
               "decoder_scheduler": decoder_scheduler,
               "super_res_scheduler": super_res_scheduler,
           }







    def        _lowercase	(       self	,    UpperCamelCase__	,    UpperCamelCase__=0	,    UpperCamelCase__=True	)	-> int:
           lowerCamelCase  : Dict     					=   floats_tensor((1, 3, 32, 32)	,    rng=random.Random(lowerCamelCase_	)	).to(lowerCamelCase_	)
           if str(lowerCamelCase_	).startswith("mps"	):
                  lowerCamelCase  : int     					=   torch.manual_seed(lowerCamelCase_	)
           else:
                  lowerCamelCase  : Tuple     					=   torch.Generator(device=lowerCamelCase_	).manual_seed(lowerCamelCase_	)

           if pil_image:
                  lowerCamelCase  : Optional[Any]     					=   input_image * 0.5 + 0.5
                  lowerCamelCase  : List[Any]     					=   input_image.clamp(0	,    1	)
                  lowerCamelCase  : Optional[Any]     					=   input_image.cpu().permute(0	,    2	,    3	,    1	).float().numpy()
                  lowerCamelCase  : List[Any]     					=   DiffusionPipeline.numpy_to_pil(lowerCamelCase_	)[0]

           return {
               "image": input_image,
               "generator": generator,
               "decoder_num_inference_steps": 2,
               "super_res_num_inference_steps": 2,
               "output_type": "np",
           }







    def        _lowercase	(       self	)	-> Tuple:
           lowerCamelCase  : Dict     					=   "cpu"

           lowerCamelCase  : Any     					=   self.get_dummy_components()

           lowerCamelCase  : int     					=   self.pipeline_class(**lowerCamelCase_	)
           lowerCamelCase  : Optional[int]     					=   pipe.to(lowerCamelCase_	)

           pipe.set_progress_bar_config(disable=lowerCamelCase_	)

           lowerCamelCase  : Union[str, Any]     					=   self.get_dummy_inputs(lowerCamelCase_	,    pil_image=lowerCamelCase_	)

           lowerCamelCase  : Tuple     					=   pipe(**lowerCamelCase_	)
           lowerCamelCase  : List[Any]     					=   output.images

           lowerCamelCase  : Optional[int]     					=   self.get_dummy_inputs(lowerCamelCase_	,    pil_image=lowerCamelCase_	)

           lowerCamelCase  : List[Any]     					=   pipe(
               **lowerCamelCase_	,    return_dict=lowerCamelCase_	,    )[0]

           lowerCamelCase  : Any     					=   image[0, -3:, -3:, -1]
           lowerCamelCase  : Optional[Any]     					=   image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)

           lowerCamelCase  : int     					=   np.array(
               [
                   0.9997,
                   0.0002,
                   0.9997,
                   0.9997,
                   0.9969,
                   0.0023,
                   0.9997,
                   0.9969,
                   0.9970,
               ]	)

           assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2







    def        _lowercase	(       self	)	-> List[Any]:
           lowerCamelCase  : Any     					=   "cpu"

           lowerCamelCase  : int     					=   self.get_dummy_components()

           lowerCamelCase  : Dict     					=   self.pipeline_class(**lowerCamelCase_	)
           lowerCamelCase  : Optional[Any]     					=   pipe.to(lowerCamelCase_	)

           pipe.set_progress_bar_config(disable=lowerCamelCase_	)

           lowerCamelCase  : List[str]     					=   self.get_dummy_inputs(lowerCamelCase_	,    pil_image=lowerCamelCase_	)

           lowerCamelCase  : Tuple     					=   pipe(**lowerCamelCase_	)
           lowerCamelCase  : List[str]     					=   output.images

           lowerCamelCase  : Tuple     					=   self.get_dummy_inputs(lowerCamelCase_	,    pil_image=lowerCamelCase_	)

           lowerCamelCase  : Optional[int]     					=   pipe(
               **lowerCamelCase_	,    return_dict=lowerCamelCase_	,    )[0]

           lowerCamelCase  : List[str]     					=   image[0, -3:, -3:, -1]
           lowerCamelCase  : Optional[Any]     					=   image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)

           lowerCamelCase  : Optional[int]     					=   np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]	)

           assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2







    def        _lowercase	(       self	)	-> Optional[int]:
           lowerCamelCase  : str     					=   "cpu"

           lowerCamelCase  : str     					=   self.get_dummy_components()

           lowerCamelCase  : Union[str, Any]     					=   self.pipeline_class(**lowerCamelCase_	)
           lowerCamelCase  : Any     					=   pipe.to(lowerCamelCase_	)

           pipe.set_progress_bar_config(disable=lowerCamelCase_	)

           lowerCamelCase  : str     					=   self.get_dummy_inputs(lowerCamelCase_	,    pil_image=lowerCamelCase_	)
           lowerCamelCase  : str     					=   [
               pipeline_inputs["image"],
               pipeline_inputs["image"],
           ]

           lowerCamelCase  : Union[str, Any]     					=   pipe(**lowerCamelCase_	)
           lowerCamelCase  : int     					=   output.images

           lowerCamelCase  : Optional[int]     					=   self.get_dummy_inputs(lowerCamelCase_	,    pil_image=lowerCamelCase_	)
           lowerCamelCase  : Any     					=   [
               tuple_pipeline_inputs["image"],
               tuple_pipeline_inputs["image"],
           ]

           lowerCamelCase  : int     					=   pipe(
               **lowerCamelCase_	,    return_dict=lowerCamelCase_	,    )[0]

           lowerCamelCase  : Tuple     					=   image[0, -3:, -3:, -1]
           lowerCamelCase  : Any     					=   image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (2, 64, 64, 3)

           lowerCamelCase  : List[str]     					=   np.array(
               [
                   0.9997,
                   0.9989,
                   0.0008,
                   0.0021,
                   0.9960,
                   0.0018,
                   0.0014,
                   0.0002,
                   0.9933,
               ]	)

           assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2







    def        _lowercase	(       self	)	-> Optional[int]:
           lowerCamelCase  : int     					=   torch.device("cpu"	)




           class 						UpperCamelCase__      :

               '''simple docstring'''



               lowerCamelCase_ :      List[Any]      	= 1




           lowerCamelCase  : Union[str, Any]     					=   self.get_dummy_components()

           lowerCamelCase  : Tuple     					=   self.pipeline_class(**lowerCamelCase_	)
           lowerCamelCase  : List[str]     					=   pipe.to(lowerCamelCase_	)

           pipe.set_progress_bar_config(disable=lowerCamelCase_	)

           lowerCamelCase  : Any     					=   torch.Generator(device=lowerCamelCase_	).manual_seed(0	)
           lowerCamelCase  : Dict     					=   pipe.decoder.dtype
           lowerCamelCase  : int     					=   1

           lowerCamelCase  : List[Any]     					=   (
               batch_size,
               pipe.decoder.config.in_channels,
               pipe.decoder.config.sample_size,
               pipe.decoder.config.sample_size,
           )
           lowerCamelCase  : int     					=   pipe.prepare_latents(
               lowerCamelCase_	,    dtype=lowerCamelCase_	,    device=lowerCamelCase_	,    generator=lowerCamelCase_	,    latents=lowerCamelCase_	,    scheduler=DummyScheduler()	)

           lowerCamelCase  : List[Any]     					=   (
               batch_size,
               pipe.super_res_first.config.in_channels // 2,
               pipe.super_res_first.config.sample_size,
               pipe.super_res_first.config.sample_size,
           )
           lowerCamelCase  : Any     					=   pipe.prepare_latents(
               lowerCamelCase_	,    dtype=lowerCamelCase_	,    device=lowerCamelCase_	,    generator=lowerCamelCase_	,    latents=lowerCamelCase_	,    scheduler=DummyScheduler()	)

           lowerCamelCase  : Union[str, Any]     					=   self.get_dummy_inputs(lowerCamelCase_	,    pil_image=lowerCamelCase_	)

           lowerCamelCase  : List[str]     					=   pipe(
               **lowerCamelCase_	,    decoder_latents=lowerCamelCase_	,    super_res_latents=lowerCamelCase_	).images

           lowerCamelCase  : Tuple     					=   self.get_dummy_inputs(lowerCamelCase_	,    pil_image=lowerCamelCase_	)
           # Don't pass image, instead pass embedding
           lowerCamelCase  : str     					=   pipeline_inputs.pop("image"	)
           lowerCamelCase  : Dict     					=   pipe.image_encoder(lowerCamelCase_	).image_embeds

           lowerCamelCase  : Any     					=   pipe(
               **lowerCamelCase_	,    decoder_latents=lowerCamelCase_	,    super_res_latents=lowerCamelCase_	,    image_embeddings=lowerCamelCase_	,    ).images

           # make sure passing text embeddings manually is identical
           assert np.abs(img_out_a - img_out_a	).max() < 1e-4







    @skip_mps
    def        _lowercase	(       self	)	-> Optional[Any]:
           lowerCamelCase  : str     					=   torch_device == "cpu"

           # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
           lowerCamelCase  : int     					=   1e-2

           self._test_attention_slicing_forward_pass(
               test_max_difference=lowerCamelCase_	,    expected_max_diff=lowerCamelCase_	)







    @skip_mps
    def        _lowercase	(       self	)	-> List[Any]:
           lowerCamelCase  : Dict     					=   torch_device == "cpu"
           lowerCamelCase  : int     					=   True
           lowerCamelCase  : Optional[Any]     					=   [
               "decoder_num_inference_steps",
               "super_res_num_inference_steps",
           ]

           self._test_inference_batch_single_identical(
               test_max_difference=lowerCamelCase_	,    relax_max_difference=lowerCamelCase_	,    additional_params_copy_to_batched_inputs=lowerCamelCase_	,    )







    def        _lowercase	(       self	)	-> List[str]:
           lowerCamelCase  : Tuple     					=   [
               "decoder_num_inference_steps",
               "super_res_num_inference_steps",
           ]

           if torch_device == "mps":
                  # TODO: MPS errors with larger batch sizes
                  lowerCamelCase  : Tuple     					=   [2, 3]
                  self._test_inference_batch_consistent(
                      batch_sizes=lowerCamelCase_	,    additional_params_copy_to_batched_inputs=lowerCamelCase_	,    )
           else:
                  self._test_inference_batch_consistent(
                      additional_params_copy_to_batched_inputs=lowerCamelCase_	)







    @skip_mps
    def        _lowercase	(       self	)	-> Dict:
           return super().test_dict_tuple_outputs_equivalent()







    @skip_mps
    def        _lowercase	(       self	)	-> Optional[Any]:
           return super().test_save_load_local()







    @skip_mps
    def        _lowercase	(       self	)	-> Any:
           return super().test_save_load_optional_components()




@slow
@require_torch_gpu
class 						UpperCamelCase__      (unittest.TestCase	):

    '''simple docstring'''



    def        _lowercase	(       self	)	-> Optional[int]:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()







    def        _lowercase	(       self	)	-> Optional[int]:
           lowerCamelCase  : int     					=   load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png"	)
           lowerCamelCase  : Tuple     					=   load_numpy(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
               "/unclip/karlo_v1_alpha_cat_variation_fp16.npy"	)

           lowerCamelCase  : Any     					=   UnCLIPImageVariationPipeline.from_pretrained(
               "kakaobrain/karlo-v1-alpha-image-variations"	,    torch_dtype=torch.floataa	)
           lowerCamelCase  : int     					=   pipeline.to(lowerCamelCase_	)
           pipeline.set_progress_bar_config(disable=lowerCamelCase_	)

           lowerCamelCase  : Optional[int]     					=   torch.Generator(device="cpu"	).manual_seed(0	)
           lowerCamelCase  : Dict     					=   pipeline(
               lowerCamelCase_	,    generator=lowerCamelCase_	,    output_type="np"	,    )

           lowerCamelCase  : Any     					=   output.images[0]

           assert image.shape == (256, 256, 3)

           assert_mean_pixel_difference(lowerCamelCase_	,    lowerCamelCase_	,    15	)







import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


_SCREAMING_SNAKE_CASE 		=      """\
@INPROCEEDINGS{Papineni02bleu:a,
    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},
    booktitle = {},
    year = {2002},
    pages = {311--318}
}
@inproceedings{lin-och-2004-orange,
    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",
    author = \"Lin, Chin-Yew  and
      Och, Franz Josef\",
    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",
    month = \"aug 23{--}aug 27\",
    year = \"2004\",
    address = \"Geneva, Switzerland\",
    publisher = \"COLING\",
    url = \"https://www.aclweb.org/anthology/C04-1072\",
    pages = \"501--507\",
}
"""

_SCREAMING_SNAKE_CASE 		=      """\
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.
Quality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,
the better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and
remains one of the most popular automated and inexpensive metrics.

Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.
Those scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness
are not taken into account[citation needed].

BLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1
representing more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the
reference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional
reference translations will increase the BLEU score.
"""

_SCREAMING_SNAKE_CASE 		=      """
Computes BLEU score of translated segments against one or more references.
Args:
    predictions: list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    max_order: Maximum n-gram order to use when computing BLEU score.
    smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
    'bleu': bleu score,
    'precisions': geometric mean of n-gram precisions,
    'brevity_penalty': brevity penalty,
    'length_ratio': ratio of lengths,
    'translation_length': translation_length,
    'reference_length': reference_length
Examples:

    >>> predictions = [
    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample
    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample
    ... ]
    >>> references = [
    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)
    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)
    ... ]
    >>> bleu = datasets.load_metric(\"bleu\")
    >>> results = bleu.compute(predictions=predictions, references=references)
    >>> print(results[\"bleu\"])
    1.0
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,     _KWARGS_DESCRIPTION		)
class SCREAMING_SNAKE_CASE_		(      datasets.Metric		):







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    return datasets.MetricInfo(
        description=_DESCRIPTION   ,   citation=_CITATION   ,   inputs_description=_KWARGS_DESCRIPTION   ,   features=datasets.Features(
            {
                """predictions""": datasets.Sequence(datasets.Value("""string"""   ,   id="""token"""				)   ,   id="""sequence"""				),
                """references""": datasets.Sequence(
                    datasets.Sequence(datasets.Value("""string"""   ,   id="""token"""				)   ,   id="""sequence"""				)   ,   id="""references"""				),
            }				)   ,   codebase_urls=["""https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"""]   ,   reference_urls=[
            """https://en.wikipedia.org/wiki/BLEU""",
            """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
        ]   ,   )







   def 						lowerCamelCase_	(     self						:						Optional[Any]   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						Optional[Any]=4   ,   lowerCamelCase_						:						List[Any]=False				):





    """simple docstring"""


    UpperCamelCase			  =      compute_bleu(
        reference_corpus=lowerCamelCase_   ,   translation_corpus=lowerCamelCase_   ,   max_order=lowerCamelCase_   ,   smooth=lowerCamelCase_				)
    ((UpperCamelCase)  ,     (UpperCamelCase)  ,     (UpperCamelCase)  ,     (UpperCamelCase)  ,     (UpperCamelCase)  ,     (UpperCamelCase))			  =      score
    return {
        "bleu": bleu,
        "precisions": precisions,
        "brevity_penalty": bp,
        "length_ratio": ratio,
        "translation_length": translation_length,
        "reference_length": reference_length,
    }
